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News/MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference
arxiv
PublishedMay 8, 2026 at 4:00 AM
▲bullish

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.05225v1 Announce Type: cross Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs) suffer from a significant efficiency bottleneck during Expert Parallelism (EP) inference due to the straggler effect. This issue is worsened in the multimodal context, as existing token-

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Discussion
Mentioned models
02
  • 01
    Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs)
  • 02
    MACS (Modality-Aware Capacity Scaling)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#multimodal#efficiency#inference#large-language-models

No replies yet. Be first.

Mentioned models
02
  • 01
    Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs)
  • 02
    MACS (Modality-Aware Capacity Scaling)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#multimodal#efficiency#inference#large-language-models
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Originally published on arxiv ↗
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